{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "MAX_SEQUENCE_LENGTH = 200 # 句子 上限200个词\n",
    "EMBEDDING_DIM = 100 # 100d 词向量"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 字向量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "good len: 1294531\n",
      "bad len: 48126\n"
     ]
    }
   ],
   "source": [
    "good = []\n",
    "bad = []\n",
    "for line in open('data/goodqueries.txt'):\n",
    "    good.append(line.strip('\\n'))\n",
    "for line in open('data/badqueries.txt'):\n",
    "    bad.append(line.strip('\\n'))\n",
    "print('good len:', len(good))\n",
    "print('bad len:', len(bad))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data: 192504\n",
      "/103886/ <svg onload=location='//p0.al'>\n"
     ]
    }
   ],
   "source": [
    "data = []\n",
    "labels = []\n",
    "\n",
    "length = len(bad)\n",
    "scale = 3\n",
    "data.extend(good[:length * scale]) # 只取部分数据\n",
    "data.extend(bad)\n",
    "labels.extend([1] * length * scale)\n",
    "labels.extend([0] * length)\n",
    "print('data:', len(data))\n",
    "print(data[0], data[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 175 unique tokens.\n",
      "Shape of data tensor: (192504, 200)\n"
     ]
    }
   ],
   "source": [
    "# https://keras-cn-docs.readthedocs.io/zh_CN/latest/blog/word_embedding/\n",
    "import numpy as np\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "# tokenizer\n",
    "texts = data\n",
    "tokenizer = Tokenizer(char_level=True) # 字向量\n",
    "tokenizer.fit_on_texts(texts)\n",
    "word_index = tokenizer.word_index\n",
    "\n",
    "# sequences\n",
    "sequences = tokenizer.texts_to_sequences(data)\n",
    "\n",
    "# padding\n",
    "data = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)\n",
    "\n",
    "print('Found %s unique tokens.' % len(word_index))\n",
    "print('Shape of data tensor:', data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "token_path = 'model/tokenizer.pkl'\n",
    "pickle.dump(tokenizer, open(token_path, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "# 打乱顺序\n",
    "index = [i for i in range(len(data))]\n",
    "random.shuffle(index)\n",
    "data = np.array(data)[index]\n",
    "labels = np.array(labels)[index]\n",
    "\n",
    "TRAIN_SPLIT = 0.8 # 20% 测试集\n",
    "TRAIN_SIZE = int(len(data) * TRAIN_SPLIT)\n",
    "\n",
    "X_train, X_test = data[0:TRAIN_SIZE], data[TRAIN_SIZE:]\n",
    "Y_train, Y_test = labels[0:TRAIN_SIZE], labels[TRAIN_SIZE:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train len: 154003\n",
      "test len: 38501\n"
     ]
    }
   ],
   "source": [
    "print('train len:', len(X_train))\n",
    "print('test len:', len(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from keras import backend as K\n",
    "\n",
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
    "\n",
    "G = 1 # GPU 数量\n",
    "gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.8)\n",
    "session = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, allow_soft_placement=True))\n",
    "K.set_session(session)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bi-LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_1 (Embedding)      (None, 200, 100)          17600     \n",
      "_________________________________________________________________\n",
      "bidirectional_1 (Bidirection (None, 128)               84480     \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 64)                256       \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 65        \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 1)                 4         \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 1)                 0         \n",
      "=================================================================\n",
      "Total params: 110,661\n",
      "Trainable params: 110,531\n",
      "Non-trainable params: 130\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Activation, BatchNormalization\n",
    "from keras.layers import Dense, LSTM\n",
    "from keras.layers.embeddings import Embedding\n",
    "from keras.layers.wrappers import Bidirectional\n",
    "\n",
    "QA_EMBED_SIZE = 64\n",
    "DROPOUT_RATE = 0.3\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Embedding(len(word_index) + 1, EMBEDDING_DIM, input_length=MAX_SEQUENCE_LENGTH))\n",
    "model.add(Bidirectional(LSTM(QA_EMBED_SIZE, return_sequences=False, dropout=DROPOUT_RATE, recurrent_dropout=DROPOUT_RATE)))\n",
    "\n",
    "model.add(Dense(QA_EMBED_SIZE))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(1))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation(\"sigmoid\"))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 模型可视化 https://keras-cn.readthedocs.io/en/latest/other/visualization/\n",
    "from keras.utils import plot_model\n",
    "from IPython import display\n",
    "\n",
    "# pip install pydot-ng\n",
    "# sudo apt-get install graphviz\n",
    "plot_model(model, to_file=\"img/model-blstm.png\", show_shapes=True)\n",
    "display.Image('img/model-blstm.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 107802 samples, validate on 46201 samples\n",
      "Epoch 1/3\n",
      "107802/107802 [==============================] - 1292s 12ms/step - loss: 0.2291 - acc: 0.9657 - precision: 0.9758 - recall: 0.9787 - f1: 0.9767 - val_loss: 0.1153 - val_acc: 0.9851 - val_precision: 0.9834 - val_recall: 0.9970 - val_f1: 0.9901\n",
      "Epoch 2/3\n",
      "107802/107802 [==============================] - 1223s 11ms/step - loss: 0.0810 - acc: 0.9880 - precision: 0.9895 - recall: 0.9945 - f1: 0.9919 - val_loss: 0.0533 - val_acc: 0.9915 - val_precision: 0.9930 - val_recall: 0.9957 - val_f1: 0.9943\n",
      "Epoch 3/3\n",
      "107802/107802 [==============================] - 1210s 11ms/step - loss: 0.0470 - acc: 0.9911 - precision: 0.9917 - recall: 0.9965 - f1: 0.9940 - val_loss: 0.0285 - val_acc: 0.9947 - val_precision: 0.9950 - val_recall: 0.9978 - val_f1: 0.9964\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fc1b475fa58>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard\n",
    "# from keras.utils import multi_gpu_model\n",
    "from evaluate import *\n",
    "\n",
    "EPOCHS = 3\n",
    "BATCH_SIZE = 64 * G\n",
    "VALIDATION_SPLIT = 0.3 # 30% 验证集\n",
    "\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=10)\n",
    "model_checkpoint = ModelCheckpoint('model/model-blstm.h5', save_best_only=True, save_weights_only=True)\n",
    "tensor_board = TensorBoard('log/tflog-blstm', write_graph=True, write_images=True)\n",
    "\n",
    "# model = multi_gpu_model(model)\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "                  optimizer='adam',\n",
    "                  metrics=['accuracy', precision, recall, f1])\n",
    "\n",
    "model.fit(X_train, Y_train, epochs=EPOCHS, batch_size=BATCH_SIZE, \n",
    "          validation_split=VALIDATION_SPLIT, shuffle=True, \n",
    "          callbacks=[early_stopping, model_checkpoint, tensor_board])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38501/38501 [==============================] - 97s 3ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.02783341104081169,\n",
       " 0.9951429832991351,\n",
       " 0.9954096649297538,\n",
       " 0.9981446988347109,\n",
       " 0.9967352647489828]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test, Y_test, verbose=1, batch_size=BATCH_SIZE)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
